Access Status

Authors

Date

Type

Metadata

Abstract

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures - the Consistency index and signal-to-noise ratio (SNR) - the use of our proposed methods significantly increased feature stability when compared with the baselines.

Related items

The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs ...

Task scheduling in parallel processing systems is one of the most challenging industrial problems. This problem typically arises in the manufacturing and service industries. The task scheduling problem is to determine a ...